Backcourt forehand clear stroke is a fundamental technique in badminton, but the biomechanical differences between novice and experienced players are not fully understood. This study aimed to compare the kinematic characteristics of body segment acceleration during the backcourt forehand clear stroke between these two groups. Forty-five participants (22 novice players with less than 2 years of experience and 23 experienced players with over 5 years of competitive experience) were placed with wireless accelerometers on key body segments (foot, calf, thigh, hip, shoulder, upper arm, forearm). Each participant performed 5 backcourt forehand clear strokes, and acceleration data were collected at 100 Hz. The mean values from the 5 repetitions were used for statistical analysis to ensure reliability and reduce variability. The results revealed significant differences in movement patterns between the groups. Novices exhibited greater ranges of motion (ROMs) in most body segments, particularly at the hip, thigh, calf, and shoulder, across the x, y, and z axes (p < 0.05). Additionally, novices showed higher maximum and minimum accelerations (p < 0.05). These findings suggest that experienced players achieve more efficient movement patterns through refined motor control and reduced excessive motion. This research provides valuable insights for coaching interventions and technique refinement in badminton training programs, particularly for developing players.

Objective: The aim of this study was to develop and evaluate binary classification models to detect physical fatigue in badminton athletes using inertial measurement unit (IMU) data and machine learning algorithms. Methods: Thirty-two collegiate badminton athletes participated in this study. Movement data of these participants were collected using multi-sensor IMUs placed on key body regions and a single forearm IMU sensor before and after fatigue induction. Feature selection was performed using Lasso regression to identify the most relevant kinematic features. Six machine learning models—support vector machine (SVM), random forest (RF), logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), and naïve Bayes (NB)—were applied to construct binary classification models to detect fatigue. Models’ performances were evaluated based on area under the curve (AUC), accuracy, sensitivity, precision, and F1 score. Results: Overall performance of the SVM model was the best across both multi-sensor and single-sensor configurations, with AUC values of 0.89 and 0.95. The single-sensor forearm IMU model achieved high predictive accuracy, underscoring the feasibility of using simplified setups for fatigue monitoring. Feature selection by Lasso regression revealed key fatigue indicators, including forearm kinematic features, contributing significantly to model accuracy. Conclusion: IMU data combined with machine learning models can reliably be used to assess physical fatigue in badminton athletes. High performances of multi-sensor and single-sensor configurations suggest flexibility in model application, supporting real-time, field-based monitoring for fatigue management, performance optimization, and injury prevention in sports. Future research should validate these models in broader athletic populations and explore additional data sources to enhance their predictive abilities.

Visual perception and gaze behavior play a crucial role in fast-paced sports such as badminton, where athletes must rapidly process visual cues to anticipa

ObjectiveTo evaluate offline binary classification of recorded fatigue conditions using inertial measurement unit (IMU) acceleration features and five machine-learning algorithms.MethodsThe author-confirmed performance dataset contained 32 observations per condition. The revised primary IMU dataset comprised 70 recordings from 25 roster-linked athletes, with 22 non-fatigue and 48 fatigue labels after correction of four misclassified recordings. Seven-sensor and right-forearm configurations provided 231 and 33 candidate features. A newly reconstructed analysis retained five documented outer athlete-grouped folds and used three grouped inner folds for training-only preprocessing, sparse logistic feature selection and hyperparameter tuning.ResultsCMJ was 40.01 ± 8.51 and 30.27 ± 7.29 in the non-fatigue and fatigue conditions, respectively. Across classifiers, fold-mean AUC ranged from 0.655 to 0.846 for seven sensors and from 0.715 to 0.846 for the right forearm. Macro F1 ranged from 0.600 to 0.775 and from 0.655 to 0.775, respectively. Specificity ranged from 0.483 to 0.700 and from 0.550 to 0.650. Code, inclusion decisions, actual inner and outer assignments and held-out predictions are supplied for this new run.ConclusionThe results support further investigation of IMU-based condition classification, but do not establish algorithm superiority, sensor equivalence, physiological validity of every recording label, or real-time monitoring performance.

ObjectiveTo evaluate offline binary classification of recorded fatigue conditions using inertial measurement unit (IMU) acceleration features and five machine-learning algorithms.MethodsThe author-confirmed performance dataset contained 32 observations per condition. The revised primary IMU dataset comprised 70 recordings from 25 roster-linked athletes, with 22 non-fatigue and 48 fatigue labels after correction of four misclassified recordings. Seven-sensor and right-forearm configurations provided 231 and 33 candidate features. A newly reconstructed analysis retained five documented outer athlete-grouped folds and used three grouped inner folds for training-only preprocessing, sparse logistic feature selection and hyperparameter tuning.ResultsCMJ was 40.01 ± 8.51 and 30.27 ± 7.29 in the non-fatigue and fatigue conditions, respectively. Across classifiers, fold-mean AUC ranged from 0.655 to 0.846 for seven sensors and from 0.715 to 0.846 for the right forearm. Macro F1 ranged from 0.600 to 0.775 and from 0.655 to 0.775, respectively. Specificity ranged from 0.483 to 0.700 and from 0.550 to 0.650. Code, inclusion decisions, actual inner and outer assignments and held-out predictions are supplied for this new run.ConclusionThe results support further investigation of IMU-based condition classification, but do not establish algorithm superiority, sensor equivalence, physiological validity of every recording label, or real-time monitoring performance.

ObjectiveTo evaluate offline binary classification of recorded fatigue conditions using inertial measurement unit (IMU) acceleration features and five machine-learning algorithms.MethodsThe author-confirmed performance dataset contained 32 observations per condition. The revised primary IMU dataset comprised 70 recordings from 25 roster-linked athletes, with 22 non-fatigue and 48 fatigue labels after correction of four misclassified recordings. Seven-sensor and right-forearm configurations provided 231 and 33 candidate features. A newly reconstructed analysis retained five documented outer athlete-grouped folds and used three grouped inner folds for training-only preprocessing, sparse logistic feature selection and hyperparameter tuning.ResultsCMJ was 40.01 ± 8.51 and 30.27 ± 7.29 in the non-fatigue and fatigue conditions, respectively. Across classifiers, fold-mean AUC ranged from 0.655 to 0.846 for seven sensors and from 0.715 to 0.846 for the right forearm. Macro F1 ranged from 0.600 to 0.775 and from 0.655 to 0.775, respectively. Specificity ranged from 0.483 to 0.700 and from 0.550 to 0.650. Code, inclusion decisions, actual inner and outer assignments and held-out predictions are supplied for this new run.ConclusionThe results support further investigation of IMU-based condition classification, but do not establish algorithm superiority, sensor equivalence, physiological validity of every recording label, or real-time monitoring performance.